---
license: other
language:
- en
- as
- bn
- brx
- doi
- gu
- hi
- kn
- ks
- kok
- mai
- ml
- mni
- mr
- ne
- or
- pa
- sa
- sat
- sd
- ta
- te
- ur
- bho
- hne
- bgc
- bhb
library_name: nemo
pipeline_tag: automatic-speech-recognition
base_model: nvidia/canary-1b-v2
tags:
- automatic-speech-recognition
- speech
- audio
- asr
- multilingual
- indic
- code-switching
- code-mixing
- romanization
- transliteration
- language-identification
- nemo
- canary
- fastconformer
metrics:
- wer
- cer
---

<h1 id="indic-transcribe-flex" style="color:#FFD21E;">Indic-Transcribe-flex</h1>

<div align="center">

[![Model Arch](https://img.shields.io/badge/Model_Arch-FastConformer--Transformer-C1440E?style=flat#model-badge)](#model-architecture)
[![Params](https://img.shields.io/badge/Params-1.2B-C1440E?style=flat#model-badge)](#model-architecture)
[![Languages](https://img.shields.io/badge/Languages-27-C1440E?style=flat#model-badge)](#supported-languages)
[![Language](https://img.shields.io/badge/Language-Multilingual-C1440E?style=flat#model-badge)](#supported-languages)
[![License](https://img.shields.io/badge/License-CC--BY--4.0-C1440E?style=flat#model-badge)](#license--terms-of-use)

</div>

**Multilingual speech recognition for 27 Indian languages — with native-script, mixed-script, and romanized output.**

<div align="center">
  <img src="model-diagram.png" alt="Indic-Transcribe: 27 languages in, all output features out" width="860">
</div>

**Quick links** — [Blog](https://unknotty-jeanett-prethyroid.ngrok-free.dev/#/research/bodhan-asr) · [Demo](https://value-candidates-oakland-capitol.trycloudflare.com/) · [Try it out](https://fotos-symposium-compensation-episode.trycloudflare.com/) · [GitHub](https://github.com/AshwinSankar17/bodhan_gen_ai_tools)

**Indic-Transcribe-pro** is a multilingual Automatic Speech Recognition (ASR) model built for **27 Indian languages**. It is trained to be robust and general purpose: it handles the full diversity of Indian accents and holds up in noisy real-world conditions, from crowded markets to call-center floors, with strong coverage in the domains where Indian voice products are actually built — education, agriculture, and healthcare.

Unlike traditional ASR systems that return only a native-script transcript, Indic-Transcribe offers **three transcription modes** — native script, romanized text, or true code-mixed output — so you can match the output to what your product expects.

This model is ready for commercial use.

---

<h2 id="why-choose-indic-transcribe" style="color:#FFD21E;">Why Choose Indic-Transcribe?</h2>

- 🔀 **Code-mixing, natively.** Indians rarely speak one language at a time. Indic-Transcribe transcribes Hinglish and other mixed speech as it is actually spoken, instead of forcing it into a single language.
- 📝 **Three transcription modes.** *Native script* for fully native output; *mixed script* for native words in native script with English and numerals in Latin; *romanized* for everything in Latin script.
- 🏥 **Domain coverage where it matters.** Deep vocabulary in education, agriculture, and healthcare.
- 🌐 **Language identification built in.** Use the model directly as a language-ID system, or let it auto-detect the language and then transcribe.
- ⚡ **Streaming for live use.** Real-time transcription for conversational use cases, available in [Indic-Transcribe-lite](https://huggingface.co/bodhan/Indic-Transcribe-lite).

---

<h2 id="supported-languages" style="color:#FFD21E;">Supported Languages</h2>

The model covers **27 languages** across four groups:

| Group | Languages |
| --- | --- |
| **Indian-accented English** | English — benchmarked across speakers from 19 states |
| **22 constitutionally recognised languages** | Assamese, Bengali, Bodo, Dogri, Gujarati, Hindi, Kannada, Kashmiri, Konkani, Maithili, Malayalam, Manipuri, Marathi, Nepali, Odia, Punjabi, Sanskrit, Santali, Sindhi, Tamil, Telugu, Urdu |
| **Hindi dialects** | Bhojpuri, Chhattisgarhi, Haryanvi |
| **Extremely low-resource** | Bhili |

See [Supported Language Codes](#supported-language-codes) for the code to pass at inference time.

<h3 style="color:#FFD21E;">The three transcription modes</h3>

The same utterance, three renderings:

| Mode | Output |
| --- | --- |
| Native script | `मैंने कल पांच बजे तीन फाइलें अपलोड कीं` |
| Mixed script | `मैंने कल 5 बजे 3 files upload कीं` |
| Romanized | `maine kal 5 baje 3 files upload kin` |

---

<h2 id="model-architecture" style="color:#FFD21E;">Model Architecture</h2>

**Architecture Type:** NVIDIA Canary — FastConformer encoder with a Transformer decoder.

Indic-Transcribe-pro is built on the [nvidia/canary-1b-v2](https://huggingface.co/nvidia/canary-1b-v2) architecture. The FastConformer encoder produces acoustic representations that the Transformer decoder converts into text, with task tokens selecting the transcription mode and target language.

|  |  |
| --- | --- |
| **Model name** | Indic-Transcribe-pro |
| **Task** | Speech-to-Text (Automatic Speech Recognition) |
| **Base model** | [nvidia/canary-1b-v2](https://huggingface.co/nvidia/canary-1b-v2) |
| **Total parameters** | 1.2B |
| **Encoder** | FastConformer — 32 layers, 811M params, 1024 hidden dim, 8 attention heads, conv kernel 9 |
| **Decoder** | Transformer — 24 layers, 419M params, 1024 hidden size, 8 attention heads |
| **Vocabulary** | 7,152 tokens (1,152 special / task + 6,000 multilingual) |
| **Sub-word algorithm** | BPE (byte fallback disabled) |
| **Precision** | fp16 |
| **Checkpoint size** | 4.6 GB |

<h3 style="color:#FFD21E;">Feature comparison</h3>

| Feature | Indic-Transcribe-flex | Indic-Transcribe-realtime |
| --- | :---: | :---: |
| Languages | 27 | 27 |
| Code-mixed audio | ✅ | ✅ |
| Inverse Text Normalization | ✅ | ❌ |
| Transcription Modes | Native Script, Mixed Script, Romanized | Native Script |
| Automatic language ID | ✅ | ❌ |
| Streaming | ❌ | ✅ |
| Latency (1×H100) | 150 ms | 35 ms |

---

<h2 id="results-at-a-glance" style="color:#FFD21E;">Results at a Glance</h2>

Results below are from the **Voice of India** benchmark. ASR performance is measured using Word Error Rate (WER); **lower is better**. Best score per column is in **bold**.

<h3 style="color:#FFD21E;">Systems covering all benchmark languages</h3>

| Model | Average | Assamese | Bhojpuri | Bengali | Gujarati | Hindi | Chhattisgarhi | Kannada | Maithili | Malayalam | Marathi | Odia | Punjabi | Tamil | Telugu | Urdu |
| --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Saaras V3 | **10.7** | 9.1 | 17.9 | 5.2 | **9.7** | 3.8 | 14.0 | 8.8 | **14.2** | 12.2 | 6.5 | 11.1 | 8.6 | 9.1 | 13.5 | 7.5 |
| **Indic-Transcribe-flex** | 11.3 | 9.6 | 18.5 | **5.0** | 10.8 | 4.1 | **13.6** | 9.7 | 15.1 | 13.7 | 6.6 | 10.2 | 9.5 | 10.8 | **13.1** | **5.6** |
| Indic Conformer | 17.8 | 13.1 | 30.3 | 9.5 | 16.3 | 6.5 | 24.5 | 16.3 | 16.3 | 28.2 | 11.6 | 13.1 | 19.1 | 16.2 | 20.0 | 8.0 |
| Gemini 3 Pro | 21.1 | 23.7 | 24.1 | 10.3 | 18.1 | 9.3 | 19.6 | 20.1 | 27.2 | 21.0 | 14.0 | 25.7 | 19.3 | 15.5 | 24.6 | 10.6 |
| Gemini 3 Flash | 23.1 | 25.0 | 20.2 | 11.1 | 20.3 | 7.1 | 21.7 | 18.5 | 28.0 | 30.1 | 14.2 | 23.3 | 23.2 | 17.3 | 25.5 | 11.0 |
| Gemma E4B | 36.1 | 45.0 | 27.0 | 19.6 | 27.3 | 9.1 | 24.0 | 31.0 | 36.7 | 44.4 | 24.4 | 44.4 | 23.4 | 37.9 | 41.6 | 14.2 |
| OmniASR LLM 7B | 44.5 | 23.9 | 26.3 | 20.9 | 32.0 | 9.6 | 20.7 | 35.0 | 44.6 | 48.8 | 24.5 | 72.3 | 31.7 | 40.6 | 48.7 | 14.8 |
| OmniASR CTC 7B | 62.5 | 33.3 | 39.2 | 43.5 | 62.5 | 20.7 | 33.4 | 50.0 | 51.6 | 61.3 | 33.6 | 90.2 | 80.7 | 58.3 | 62.1 | 89.3 |

<!-- ---

<h2 id="throughput--efficiency" style="color:#FFD21E;">Throughput & Efficiency</h2>

Measured on a single NVIDIA H100, fp16.

| Metric | Indic-Transcribe-pro | Indic-Transcribe-lite |
| --- | :---: | :---: |
| Latency | 100 ms | 30 ms |
| RTFx | 500 | 1000 |
| Peak GPU memory | *TBD* | *TBD* |
| Checkpoint size | 4.6 GB | *TBD* |

> RTFx is the inverse real-time factor: RTFx 500 means one hour of audio is transcribed in ~7.2 seconds. -->

---

<h2 id="how-to-use-this-model" style="color:#FFD21E;">How to Use this Model</h2>

<h3 style="color:#FFD21E;">Installation</h3>

```bash
pip install torch torchaudio transformers sentencepiece soundfile
```

The model code ships inside this repository, so there is nothing else to install — no NeMo,
no other toolkit.

<details>
<summary>Conda environment (recommended for reproducibility)</summary>

```bash
conda create -n indic-transcribe python=3.10 -y
conda activate indic-transcribe
pip install torch torchaudio transformers sentencepiece soundfile
```

</details>

<h3 style="color:#FFD21E;">Input audio requirements</h3>

|  |  |
| --- | --- |
| Sample rate | 16 kHz (resampled automatically if it differs) |
| Channels | Mono |
| Formats | `.wav`, `.flac`, `.mp3` |
| Speakers | Single speaker — see [Limitations](#limitations) |

```bash
# Convert anything to the expected format
ffmpeg -i input.mp3 -ac 1 -ar 16000 -c:a pcm_s16le audio.wav
```

<h3 style="color:#FFD21E;">Basic inference</h3>

```python
from huggingface_hub import snapshot_download
from indic_transcribe import IndicTranscribe

asr = IndicTranscribe.from_pretrained(snapshot_download("bodhan-ai/indic-transcribe-pro"))
print(asr("audio.wav", lang="hi"))
```

<h3 style="color:#FFD21E;">The three transcription modes</h3>

The same audio, three outputs. Pick the mode that matches what your downstream system expects.

**Native script** — everything in the language's own script. Recommended for production.

```python
print(asr("audio.wav", lang="hi", mode="native"))
# मैंने कल पांच बजे तीन फाइलें अपलोड कीं
```

**Mixed script (ITN)** — native words in native script; English words and numerals in Latin. Inverse text normalization turns spoken numbers into digits. Recommended if you want formatted and normalized output.

```python
print(asr("audio.wav", lang="hi", mode="mixed"))
# मैंने कल 5 बजे 3 files upload कीं
```

**Romanized** — everything transliterated into Latin script. Useful for search indexing, keyword spotting, and Latin-only UIs.

```python
print(asr("audio.wav", lang="hi", mode="romanized"))
# maine kal 5 baje 3 files upload kin
```

<h3 style="color:#FFD21E;">Automatic language ID + transcription</h3>

When you don't know the language ahead of time, omit `lang`. The model identifies it first,
then transcribes — at the cost of one decoder step, not a second encoder pass.

```python
text, lid = asr.transcribe("unknown_language.wav", return_lid=True)
print(lid["lang"])   # e.g. "ta"
print(text)
```

A language you supply always wins; identification only fills a gap. `return_lid=True` also
works when you *did* supply one, so a disagreement between your metadata and the model stays
visible instead of silent:

```python
text, lid = asr.transcribe("audio.wav", lang="hi", return_lid=True)
# lid == {"lang": "hi", "source": "explicit", "topk": [("hi", 0.9999), ("ur", 0.0001), ...]}
```

<h3 style="color:#FFD21E;">Language identification only</h3>

To use the model purely as a language-ID system, read the predicted language and discard the transcript.

```python
for path in ["a.wav", "b.wav", "c.wav"]:
    print(path, asr.identify(path))
    # [('ta', 0.9812), ('ml', 0.0104), ('kn', 0.0031), ...]
```

`identify` returns the ranked distribution rather than a single string, because for the
confusable pairs the top-1 alone hides how close the decision was. Accuracy is uneven:
`ml`/`ta` 0.98 and `kn`/`bn` 0.96, against `bho` 0.05, `hi` 0.26, `mai` 0.36 and `ur` 0.49,
each absorbed by a close neighbour. If you have a language label, pass it.


<h3 style="color:#FFD21E;">Streaming</h3>

Streaming is available in **[Indic-Transcribe-lite](https://huggingface.co/bodhan/Indic-Transcribe-lite)**, not in `pro`. Use `lite` for live and conversational use cases where 30 ms latency matters more than peak accuracy.

---

<h2 id="inputs" style="color:#FFD21E;">Input(s)</h2>

| Field | Details |
| :--- | :--- |
| **Input Type(s)** | Audio, Language ID |
| **Input Format(s)** | `.wav`, `.flac`, `.mp3`; string language code |
| **Input Parameters** | One-dimensional (1D) audio; one-dimensional (1D) language ID |
| **Other Properties** | 16 kHz mono; audio is resampled automatically if it differs. Single speaker. |

---

<h2 id="output" style="color:#FFD21E;">Output</h2>

| Field | Details |
| :--- | :--- |
| **Output Type(s)** | Text string in the input language |
| **Output Format(s)** | String |
| **Output Parameters** | One-dimensional (1D) |
| **Other Properties** | Selectable native-script, mixed-script, or romanized rendering; optional detected-language tag. |

---

<h2 id="software-integration" style="color:#FFD21E;">Software Integration</h2>

**Runtime Engine:** NVIDIA NeMo (ASR collection)

**Supported Hardware Microarchitecture Compatibility:**

- NVIDIA Ampere
- NVIDIA Hopper
- NVIDIA Lovelace

**Supported Operating System(s):** Linux

---

<h2 id="supported-language-codes" style="color:#FFD21E;">Supported Language Codes</h2>

Pass these to `source_lang`. Use `"auto"` for automatic language identification.

| Example | Value |
| --- | --- |
| Hindi | `source_lang="hi"` |
| Tamil | `source_lang="ta"` |
| Bengali | `source_lang="bn"` |
| Indian English | `source_lang="en"` |
| Auto-detect | `source_lang="auto"` |

All 27 languages use standard ISO 639-1 / 639-3 codes (also listed in the `language:` field at the top of this card).

<details>
<summary>Full list of language codes (27)</summary>

| Language | Code | Script | Group |
| --- | --- | --- | --- |
| English (Indian) | `en` | Latin | Indian-accented English |
| Assamese | `as` | Bengali–Assamese | Scheduled |
| Bengali | `bn` | Bengali | Scheduled |
| Bodo | `brx` | Devanagari | Scheduled |
| Dogri | `doi` | Devanagari | Scheduled |
| Gujarati | `gu` | Gujarati | Scheduled |
| Hindi | `hi` | Devanagari | Scheduled |
| Kannada | `kn` | Kannada | Scheduled |
| Kashmiri | `ks` | Perso-Arabic / Devanagari | Scheduled |
| Konkani | `kok` | Devanagari | Scheduled |
| Maithili | `mai` | Devanagari | Scheduled |
| Malayalam | `ml` | Malayalam | Scheduled |
| Manipuri | `mni` | Bengali / Meetei Mayek | Scheduled |
| Marathi | `mr` | Devanagari | Scheduled |
| Nepali | `ne` | Devanagari | Scheduled |
| Odia | `or` | Odia | Scheduled |
| Punjabi | `pa` | Gurmukhi | Scheduled |
| Sanskrit | `sa` | Devanagari | Scheduled |
| Santali | `sat` | Ol Chiki | Scheduled |
| Sindhi | `sd` | Perso-Arabic / Devanagari | Scheduled |
| Tamil | `ta` | Tamil | Scheduled |
| Telugu | `te` | Telugu | Scheduled |
| Urdu | `ur` | Perso-Arabic | Scheduled |
| Bhojpuri | `bho` | Devanagari | Hindi dialect |
| Chhattisgarhi | `hne` | Devanagari | Hindi dialect |
| Haryanvi | `bgc` | Devanagari | Hindi dialect |
| Bhili | `bhb` | Devanagari | Low resource |

</details>

---

<h2 id="limitations" style="color:#FFD21E;">Limitations</h2>

- **Single-speaker audio.** The model is trained for single-speaker recordings. For multi-speaker scenarios, pair it with a diarization module and transcribe each speaker turn separately.
- **Mixed-script (ITN) mode can be wrong.** Inverse text normalization is genuinely ambiguous in places, so mixed-script mode will occasionally get it wrong. For production, native-script mode is recommended, applying your own ITN downstream if you need it.
- **Romanization is not standardized.** There is no single canonical romanization for most Indian languages. Romanized output follows the model's learned convention, which may not match your existing transliteration scheme.
- **Streaming is not available in `pro`.** Use Indic-Transcribe-lite for real-time use.
- **Low-resource languages are weaker.** Bhili in particular has very little training data. Check the per-language numbers in [Results at a Glance](#results-at-a-glance) before committing to a language.
- **Noise robustness has limits.** Heavily overlapped speech, far-field audio, and very low-SNR recordings will still degrade quality.

---

<h2 id="license--terms-of-use" style="color:#FFD21E;">License / Terms of Use</h2>


Released under [BODHAN AI OPEN MODEL LICENSE 1.0]().

The base model, [nvidia/canary-1b-v2](https://huggingface.co/nvidia/canary-1b-v2), carries its own license terms — ensure your use complies with both.

---

<h2 id="use-case" style="color:#FFD21E;">Use Case</h2>

Transcription of multilingual and code-mixed Indian-language audio, including native-script, mixed-script, and romanized output, plus language identification.

---

<h2 id="deployment-geography" style="color:#FFD21E;">Deployment Geography</h2>

Global

---

<h2 id="citation" style="color:#FFD21E;">Citation</h2>

```bibtex
@misc{indictranscribe2026,
  title  = {Indic-Transcribe: Multilingual Speech Recognition for 27 Indian Languages},
  author = {TODO},
  year   = {2026},
  url    = {https://huggingface.co/bodhan/Indic-Transcribe-pro}
}
```

---

<h2 id="ethical-considerations" style="color:#FFD21E;">Ethical Considerations</h2>

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Developers should work with their team to ensure this model meets requirements for the relevant industry and use case, and addresses unforeseen product misuse.